Millions of Fans Organize Collective Actions to Influence Algorithms and Boost Idol Visibility Across Platforms and Cultures

核心概念

Millions of fans organize large-scale collective actions to strategically influence algorithms across social media platforms and cultures in order to enhance the visibility of their idols.

要約

This study investigates how millions of fans, led by a small group of core fans, develop a collective understanding of algorithms and organize coordinated algorithmic actions to boost the visibility of their idols across various social media platforms and cultures.

The key findings are:

1. Fans recognize the necessity of collective algorithmic action, as they understand that their idol's data performance on social media significantly impacts commercial opportunities and public exposure. Fans believe their collective actions can influence platform algorithms.

2. Core fans develop a detailed "folk theory" of how algorithms work on different platforms and in different cultural contexts through continuous experimentation and adaptation. They then guide general fans in executing these algorithmic actions.

3. Core fans employ rhetorical strategies to persuade general fans, build a shared understanding of algorithms, and coordinate collaborative processes across platforms and cultures to execute successful collective algorithmic actions.

4. Fans leverage platform transparency, or lack thereof, to devise effective algorithmic manipulation tactics. They adapt their strategies based on the specific algorithmic policies and user requirements of each platform.

5. Fans' collective algorithmic actions are capable of mobilizing millions of participants and generating significant real-world impacts, challenging the traditional view of collective action dilemmas in large groups.

統計

"Brands value an idol's data for gauging public visibility, reputation, and fan loyalty. Better data suggests more advantages for the idol."
"When our idol is chosen as a brand ambassador, our data performance efforts indirectly benefit the brand, as we engage in activities like reposting, liking, and commenting on specific posts or videos."
"An idol's social media engagement data is closely tied to their public visibility and reputation. Typically, better engagement data equates to stronger publicity."

引用

"First, we need to understand that developing algorithms is beneficial for idols, right? And this benefit is quite significant; otherwise, we wouldn't be working so hard on this. Second, we must be clear about the practical impact of our approach; we can't just run around like headless chickens."
"Brands value an idol's data for gauging public visibility, reputation, and fan loyalty. Better data suggests more advantages for the idol."
"When our idol is chosen as a brand ambassador, our data performance efforts indirectly benefit the brand, as we engage in activities like reposting, liking, and commenting on specific posts or videos."
"An idol's social media engagement data is closely tied to their public visibility and reputation. Typically, better engagement data equates to stronger publicity."

抽出されたキーインサイト

Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions

by Qing Xiao, Y... 場所 arxiv.org 09-18-2024

https://arxiv.org/pdf/2409.10670.pdf

深掘り質問

How can platform operators better engage with fan communities to develop more transparent and accountable algorithmic systems?

Platform operators can enhance engagement with fan communities by implementing several strategies aimed at fostering transparency and accountability in algorithmic systems. Firstly, they should establish open communication channels that allow fans to provide feedback on algorithmic changes and their impacts. This could involve regular surveys, forums, or dedicated feedback sections where fans can express their concerns and suggestions regarding algorithmic functionalities. Secondly, operators can create educational resources that demystify algorithms, explaining how they work and the factors influencing content visibility. By providing clear guidelines and tutorials, fans can better understand the algorithms and their implications, which aligns with the study's findings on the importance of folk theories of algorithms among fan communities.
Additionally, platform operators should consider involving fan representatives in the decision-making processes related to algorithm updates. This participatory approach can help ensure that the interests and needs of fan communities are taken into account, leading to more user-centered algorithmic designs. Furthermore, operators can implement transparency reports that detail algorithm performance, changes, and their effects on content visibility, thereby holding themselves accountable to the user base. By fostering a collaborative environment where fans feel valued and informed, platform operators can build trust and enhance the overall user experience.

What are the potential risks and unintended consequences of large-scale, fan-driven collective algorithmic actions, and how can they be mitigated?

Large-scale, fan-driven collective algorithmic actions pose several risks and unintended consequences that need careful consideration. One significant risk is the potential for algorithmic manipulation to lead to misinformation or the promotion of harmful content. As fans engage in coordinated actions to boost visibility for their idols, there is a possibility that these tactics could inadvertently amplify misleading narratives or negative behaviors, such as cyberbullying or harassment of rival fan groups. To mitigate this risk, platforms should implement stricter content moderation policies and develop algorithms that prioritize the quality and credibility of content over sheer engagement metrics.
Another unintended consequence is the reinforcement of echo chambers, where fans may become isolated within their communities, leading to a narrow understanding of diverse perspectives. This can be addressed by encouraging cross-community interactions and promoting content that represents a variety of viewpoints. Platforms can facilitate this by creating features that highlight diverse content and encourage users to engage with materials outside their immediate interests.
Moreover, the phenomenon of "data labor" among fans can lead to burnout and exploitation, as fans may feel pressured to continuously engage in algorithmic actions to support their idols. To counteract this, platforms should consider implementing features that allow fans to set limits on their engagement activities and promote healthy online behaviors. By recognizing and addressing these risks, platforms can create a safer and more inclusive environment for fan-driven collective actions.

How might the insights from this study on fan-driven collective algorithmic actions inform the design of future human-algorithm interaction systems that empower users to collectively influence algorithms in ethical and constructive ways?

The insights from the study on fan-driven collective algorithmic actions can significantly inform the design of future human-algorithm interaction systems by emphasizing the need for user empowerment, transparency, and ethical considerations. One key takeaway is the importance of developing systems that facilitate collective understanding of algorithms among users. Future designs could incorporate educational modules that help users grasp how algorithms function and how their interactions can influence outcomes. This aligns with the study's findings on the necessity of building a collective folk understanding of algorithms.
Additionally, interaction systems should be designed to support collaborative actions among users, allowing them to organize and execute collective efforts in a constructive manner. Features such as shared dashboards, collaborative planning tools, and real-time feedback mechanisms can enable users to coordinate their actions effectively while maintaining ethical standards. By providing users with the tools to engage in collective algorithmic actions responsibly, platforms can foster a sense of community and shared purpose.
Moreover, the design of these systems should prioritize ethical considerations by incorporating safeguards against manipulation and exploitation. This could involve implementing algorithmic fairness checks, ensuring that collective actions do not inadvertently harm marginalized groups or promote negative behaviors. By integrating these insights into the design of human-algorithm interaction systems, platforms can create environments that empower users to influence algorithms positively and ethically, ultimately enhancing user agency and satisfaction.